Using Patients Like My Patient for Clinical Decision Support: Institution-Specific Probability of Celiac Disease Diagnosis Using Simplified Near-Neighbor Classification

Shirts, Brian H.; Bennett, Sterling T.; Jackson, Brian R.

doi:10.1007/s11606-013-2443-z

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…[7] Many laboratory utilizations and management proofs of principle papers have been published. [1] Any potential proof of principle project at an individual institution raises questions about generalizability of the findings and the possibility of scaling the interventions to implement similar solutions at other institutions.…”

Section: Reports Of Presentationsmentioning

confidence: 99%

“…Brian Shirts, Assistant Professor of Laboratory Medicine at the University of Washington presented data illustrating that there is substantial variability in laboratory data on patient, laboratory, and health system levels. [ 7 ] Many laboratory utilizations and management proofs of principle papers have been published. [ 1 ] Any potential proof of principle project at an individual institution raises questions about generalizability of the findings and the possibility of scaling the interventions to implement similar solutions at other institutions.…”

Section: Reports Of Presentationsmentioning

confidence: 99%

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Clinical laboratory analytics: Challenges and promise for an emerging discipline

Shirts

Jackson

Baird

et al. 2015

Journal of Pathology Informatics

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The clinical laboratory is a major source of health care data. Increasingly these data are being integrated with other data to inform health system-wide actions meant to improve diagnostic test utilization, service efficiency, and “meaningful use.” The Academy of Clinical Laboratory Physicians and Scientists hosted a satellite meeting on clinical laboratory analytics in conjunction with their annual meeting on May 29, 2014 in San Francisco. There were 80 registrants for the clinical laboratory analytics meeting. The meeting featured short presentations on current trends in clinical laboratory analytics and several panel discussions on data science in laboratory medicine, laboratory data and its role in the larger healthcare system, integrating laboratory analytics, and data sharing for collaborative analytics. One main goal of meeting was to have an open forum of leaders that work with the “big data” clinical laboratories produce. This article summarizes the proceedings of the meeting and content discussed.

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Section: Reports Of Presentationsmentioning

confidence: 99%

Section: Reports Of Presentationsmentioning

confidence: 99%

Clinical laboratory analytics: Challenges and promise for an emerging discipline

Shirts

Jackson

Baird

et al. 2015

Journal of Pathology Informatics

Self Cite

View full text Add to dashboard Cite

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“…34 Researchers in Utah have developed a novel approach to facilitating CD screening by using the results of tissue transglutaminase (tTG) IgA and positive duodenal biopsies from a regional healthcare system database to build a predictive model for near-neighbor-derived probability estimates. 35 Locally derived probabilities are likely more accurate than published estimates and are useful to screen potential CD cases and improve treatment outcomes in the community.…”

Section: Introductionmentioning

confidence: 99%

Celiac & Gluten Intolerance: A Wellness Perspective

Grace-Farfaglia¹

2014

JNHFE

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“…8 The nearest-neighbors matching approach has been used in a variety of fields, including ecology 9 and biology. 10 In epidemiology, this approach has been used in cross-sectional and quasi-experimental studies, 11,12 but not commonly for case-control studies. We hypothesized that nearest-neighbors matching could improve case-control matching and covariate adjustment compared with other methods.…”

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confidence: 99%

Nearest-Neighbors Matching for Case–Control Study Analyses: Better Risk Factor Identification From a Study of Sporadic Campylobacteriosis in the United States

et al. 2022

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Background: Case–control studies are commonly used to explore factors associated with enteric bacterial diseases. Control of confounding is challenging due to a large number of exposures of interest and the low frequencies of many of them. Methods: We evaluated nearest-neighbors matching in a case–control study (originally 1:1 matched, published in 2004) of sporadic Campylobacter infections that included information on 433 exposures in 2632 subjects during 1998–1999. We performed multiple imputations of missing data (m = 100) and calculated Gower distances between cases and controls using all possible confounders for each exposure in each dataset. We matched each case with ≤20 controls within a data-determined distance. We calculated odds ratios and population attributable fractions (PAFs). Results: Examination of pairwise correlation between exposures found very strong associations for 1046 pairs of exposures. More than 100 exposures were associated with campylobacteriosis, including nearly all risk factors identified using the previously published approach that included only 16 exposures and some less studied, rare exposures such as consumption of chicken liver and raw clams. Consumption of chicken and nonpoultry meat had the highest PAFs (62% and 59%, respectively). Conclusions: Nearest-neighbors matching appear to provide an improved ability to examine rare exposures and better control for numerous highly associated confounders.

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Using Patients Like My Patient for Clinical Decision Support: Institution-Specific Probability of Celiac Disease Diagnosis Using Simplified Near-Neighbor Classification

Cited by 8 publications

References 21 publications

Clinical laboratory analytics: Challenges and promise for an emerging discipline

Clinical laboratory analytics: Challenges and promise for an emerging discipline

Celiac & Gluten Intolerance: A Wellness Perspective

Nearest-Neighbors Matching for Case–Control Study Analyses: Better Risk Factor Identification From a Study of Sporadic Campylobacteriosis in the United States

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